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Sampling, sample preparation, and analytical variability associated with testing wheat for deoxynivalenol.

T B Whitaker1, W M Hagler, F G Giesbrecht

  • 1US Department of Agriculture, Agricultural Research Service, North Carolina State University, Raleigh 27695-7625, USA.

Journal of AOAC International
|October 26, 2000
PubMed
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Testing wheat for deoxynivalenol (DON) showed low overall variability. Sampling, preparation, and analysis each contributed to the total error, with sample preparation being the largest component.

Area of Science:

  • Food Science
  • Analytical Chemistry
  • Agricultural Science

Background:

  • Deoxynivalenol (DON) is a significant mycotoxin in wheat, posing risks to food safety and trade.
  • Accurate testing is crucial for regulatory compliance and risk management.
  • Understanding variability in DON testing is essential for method validation and quality control.

Purpose of the Study:

  • To quantify the variability in deoxynivalenol (DON) testing in wheat.
  • To partition total variability into sampling, sample preparation, and analytical components.
  • To develop predictive equations for variance components and assess factors influencing variability.

Main Methods:

  • Utilized a 0.454 kg wheat sample, Romer mill, and 25 g subsample.
  • Employed the Romer Fluoroquant analytical method for DON quantification.

Related Experiment Videos

  • Applied regression techniques to model variance components as a function of DON concentration.

    • Main Results:

    • The overall coefficient of variation (CV) for DON testing in wheat at 5 ppm was 13.4%.
    • Individual CVs were 6.3% for sampling, 10.0% for sample preparation, and 6.3% for analysis.
    • Sample preparation contributed the largest portion of the total variability.

    Conclusions:

    • DON testing in wheat exhibits relatively low variability compared to other mycotoxin testing procedures.
    • Even with a small sample size, sampling was not the dominant source of error.
    • The developed equations can predict variance components and inform strategies to reduce testing uncertainty.